EvoloPy Demo: An open source nature-inspired optimization toolbox for global optimization in Python

As an initiative to keep an implementation of the recent nature-inspired metaheuristics as well as the classical ones in a single open source framework, we introduce EvoloPy. EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent metaheuristic algorithms.

The goal of the framework is to take advantage of the rapidly growing scientific community of Python and provide a set of robust optimizers as free and open source software. We believe that implementing such algorithms in Python will increase their popularity and portability among researchers and non-specialists coming from different domains. The powerful libraries and packages available in Python (such as NumPy) will make it more feasible to apply metaheuristic algorithms for solving complex problems on a much higher scale. EvoloPy facilitates designing new algorithms or improving, hybridizing, and analyzing the current ones.

[teamshare]

Thank you for providing this video to explain the tools. It helps a lot. A suggestion is please consider to sample data and the usage.

[bibhudas]

How can we get the optimal values for the variables in the objective function?? Please suggest.

[usthbtech]

i want to apply gwo in weka, can you help me, thanks

[babithalincy]

shall we use this for image classification problems?

[SuryaUday]

How can we use the our own data with these functions or load any datasets like california housing etc? It is shown in the google collab code but not sure how to use it. Is there an example code snippet ?